NEO Workshop

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Author

Amélie Lehuen

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1 Introduction

1.0.1 Home made functions

The aim of the workshop is to…

2 SNOs datasets

All data treatment has been conducted with R version 4.2.2 (2022-10-31 ucrt) (Details available in Supplementary Data). Significance levels are tagged for p < .0001 with “****”, p < .001 with “***”, p < .01 with “**”, p < .05 with “*”. A quick report of each data set made by the package DataExplorer are available in docs/ folder.

2.1 BENTHOBS

Data are available on https://data.benthobs.fr/. There are different files:

  • granulometry TSV file with granulometry data.

  • macrofauna TSV file with macrofauna data.

  • organicmatter TSV file with organic matter data

The BENTHOBS data set count 3 tables that has the common variables:

bdd_base, bdd_table, Laboratory, Survey, station, latitude, longitude, sampling_date, Data_source, dfMonth, dfMonthtxt, dfYear, dfQuarter, column_label, Int_Area_S, Int_Area_M, Int_Area_L

  • bo_granu (n= 1,580) contains 30 variables. Period covered is from 1977 to 2021, sampling are made mainly at the months of NA, on 9 different stations, with a mean of 5 per year.

  • bo_macro (n= 42,556) contains 39 variables. Period covered is from 1997 to 2021, sampling are made mainly at the months of NA, on 18 different stations, with a mean of 6 per year. The density_ind_m2 (ind.m-2) was calculated by dividing the Count (ind) by the Sample size

  • bo_orga (n= 253) contains 22 variables. Period covered is from 2004 to 2020, sampling are made mainly at the months of NA, on 7 different stations, with a mean of 4 per year.

Table 1: Summaries of datatables from data set
sno_name obsnbtot varnbtot yearsrange nbstatmt nbstattot nbstatyr
bo_granu 1,580 30 1977 to 2021 Mar, Apr, Oct, May 9 5
bo_macro 42,556 39 1997 to 2021 Mar, Sep, Oct 18 6
bo_orga 253 22 2004 to 2020 Mar, Apr, Oct 7 4

2.2 PHYTOBS

Data are available on https://data.phytobs.fr/ (PHYTOBS 2021). There are different files:

  • Analyst files containing single taxon counts.

  • Phytobs files containing single counts for taxon groups that are part of the SNO labelled taxon groups.

  • combined files aggregating the two previous tables.

The PHYTOBS data set extractetd was only Phytobs table.

  • po_phyto (n= 170,776) contains 53 variables. Period covered is from 1987 to 2019, sampling are made mainly at the months of NA, on 23 different stations, with a mean of 14 per year.
Table 2: Summaries of datatables from data set
sno_name obsnbtot varnbtot yearsrange nbstatmt nbstattot nbstatyr
po_phyto 170,776 53 1987 to 2019 Jan, Feb, Mar, Apr, May, Jun, Jul 23 14

2.3 SOMLIT

Data are available on https://www.somlit.fr/demande-de-donnees/. You have to request with your mail each files available. Please refer to (Liénart et al. 2017), (Liénart et al. 2018), (Cocquempot et al. 2019) and (Lheureux et al. 2022) for detail about the dataset building and history. Information on dataset : https://www.ir-ilico.fr/?SOMLIT_InteretScientifique
SOMLIT parametres

Parameters available are in ?@fig-parsom. The SOMLIT data set count 3 tables that has the common variables :

bdd_base, bdd_table, longitude, latitude, station, dfYear, dfQuarter, dfMonth, dfMonthtxt, sampling_date, ID_SITE, column_label, Int_Area_S, Int_Area_M, Int_Area_L

  • sl_ctd (n= 75,248) contains 31 variables. Period covered is from 1994 to 2023, sampling are made mainly at the months of NA, on 18 different stations, with a mean of 8 per year. The table was modified by splitting the depth information (PROFONDEUR) into 25 levels of ~10m and then summarised by day and depth level (median md_d, ~min mi_d: 0.01th quantile and ~max mx_d: 0.99th quantile).

  • sl_hydro (n= 17,271) contains 50 variables. Period covered is from 1993 to 2023, sampling are made mainly at the months of NA, on 21 different stations, with a mean of 14 per year. The COEF_MAREE values at 0 were replaced by NA, the same for the MAREE at "inc".

  • sl_piconano (n= 392) contains 72 variables. Period covered is from 2021 to 2022, sampling are made mainly at the months of NA, on 17 different stations, with a mean of 14 per year. The COEF_MAREE values at 0 were replaced by NA, the same for the MAREE at "inc".

Table 3: Summaries of datatables from data set
sno_name obsnbtot varnbtot yearsrange nbstatmt nbstattot nbstatyr
sl_ctd 75,248 31 1994 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 18 8
sl_hydro 17,271 50 1993 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 21 14
sl_piconano 392 72 2021 to 2022 May, Jun, Jul, Sep, Oct 17 14

2.4 COASTHF

Data are available on https://data.coriolis-cotier.org/fr. In the menu, the active platform toggle button is activated and the COASTHF network is selected. All available stations has been selected. Detailed information are available on https://coast-hf.fr/. The selected buoys in data are listed in Table 4. Among variables, there are some “_ADJUSTED” field, that correspond to data that have been validated (qualified), otherwise, data are raw (only aberrant data removed). QC column caracterize the data quality, with a score from 1 (good) to 9 (bad) for each column, including date, station… Variables with LEVEL0 or LEVEL-1 suffixes are atmospheric, LEVEL1 a superficial (2-3m deep), LEVEL3 are at the bottom. Data are summarised per day to reduce volume and time process, with a median (x.md_d), max (x.mx_d) and min (x.mi_d) per day.

Table 4: List of buoys
Code Name
EXIN0003 POEM
EXIN0004 SOLEMIO
EXIN0002 EOL
EXIN0001 ARCACHON B13
6100284 Mesurho
EXIN0006 SOLA
6200021 Vilaine Molit
IF000700 SMART
6200450 Iroise Stanne
6200310 Smile LucSurMer
SCENES SCENES
6200443 Carnot
EXIN0005 ASTAN

The COASTHF data set count 13 tables that has the common variables :

bdd_base, bdd_table, PLATFORM, longitude, latitude, station, dfYear, dfQuarter, dfMonth, dfMonthtxt, sampling_date, column_label, Int_Area_S, Int_Area_M, Int_Area_L

Table 5: Summaries of datatables from data set
sno_name obsnbtot varnbtot yearsrange nbstatmt nbstattot nbstatyr
POEM 993 66 2017 to 2022 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
SOLEMIO 1,223 48 2005 to 2022 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
EOL 2,883 27 2013 to 2022 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
ARCACHON B13 1,298 24 2017 to 2022 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
Mesurho 4,476 558 2009 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
SOLA 483 51 2021 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
Vilaine Molit 4,141 156 2008 to 2022 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
SMART 2,533 36 2016 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
Iroise Stanne 8,068 132 2000 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
Smile LucSurMer 2,351 171 2015 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
SCENES 1,309 81 2017 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
Carnot 5,674 108 2004 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1
ASTAN 979 33 2019 to 2023 Jan, Feb, Mar, Apr, May, Jun, Jul 1 1

3 Global data sets description

Correction global rename of fields after run

3.1 Map of sites

Figure 1: Maps of data sets locations

3.2 HF data treatment to BF

Example of treatments: http://r-statistics.co/Time-Series-Analysis-With-R.html https://rc2e.com/timeseriesanalysis

sno_set is the data as they are recorded. The _hf subset is a simple summary per day. The _mf subset is a summary per month of the year before, meaning, for each month summary, the median/min/max of the data in one year finishing the month previous the record month (add of subscript .md_y, .mi_y, .mx_y). The _bf subset is a summary per quarter of the year before, calculated the same way.

3.3 SNOs timeline description

All records of all tables of all SNO are represented in Figure 2.

Figure 2: Time series of each SNO’s tables

4 Timeline region focus

Data can then be distinguished by geographical areas, with the different scale chosen: first the large one (Figure 3) that emphasize that the Channel, the Atlantic and north Brittany are of the most interest. The small scale (Figure 4) is the preferred scale reveal the challenge of the workshop. The intermediate scale (?@fig-medium_data) shows that when there is geographically close SNO data sets, they can be temporarily not that relevant.

Figure 3: Time series of SNOs tables by large area

4.1 Small area time series

(a) “Subcaption for panel (a)”

(b) “Subcaption for panel (a)”

Figure 4: Time series of SNOs tables by small area

Figure 5: Time series of SNOs tables by small area

Figure 6: Time series of SNOs tables by small area

Figure 7: Time series of SNOs tables by small area

Figure 8: Time series of SNOs tables by small area

Figure 9: Time series of SNOs tables by small area

Figure 10: Time series of SNOs tables by small area

Figure 11: Time series of SNOs tables by small area

Figure 12: Time series of SNOs tables by small area

4.2 Medium area time series

Figure 13: Time series of SNOs tables by small area

Figure 14: Time series of SNOs tables by small area

Figure 15: Time series of SNOs tables by small area

Figure 16: Time series of SNOs tables by small area

Figure 17: Time series of SNOs tables by small area

Figure 18: Time series of SNOs tables by small area

Figure 19: Time series of SNOs tables by small area

Figure 20: Time series of SNOs tables by small area

Figure 21: Time series of SNOs tables by small area

Figure 22: Time series of SNOs tables by small area

5 Small areas of interest detailed analysis

5.1 Manche

5.1.1 Estuaire de la Liane

(a) Time Line

(b) Time Line

Figure 23: Small area details data

5.1.2 Cabourg

(a) Time Line

(b) Time Line

Figure 24: Small area details data

5.1.3 Luc sur Mer

(a) Time Line

(b) Time Line

Figure 25: Small area details data

5.2 Nord Bretagne

5.2.1 Rade de Camaret

(a) Time Line

(b) Time Line

Figure 26: Small area details data

5.2.2 Baie de Daoulas

(a) Time Line

(b) Time Line

Figure 27: Small area details data

5.2.3 La Rance

(a) Time Line

(b) Time Line

Figure 28: Small area details data

5.2.4 Hebihens

(a) Time Line

(b) Time Line

Figure 29: Small area details data

5.2.5 Baie de Morlaix

(a) Time Line

(b) Time Line

Figure 30: Small area details data

5.2.6 Riviere de Morlaix

(a) Time Line

(b) Time Line

Figure 31: Small area details data

5.3 Atlantique

5.3.1 Antioche

(a) Time Line

(b) Time Line

Figure 32: Small area details data

5.3.2 Embouchure de Gironde

(a) Time Line

(b) Time Line

Figure 33: Small area details data

5.3.3 Comprian

(a) Time Line

(b) Time Line

Figure 34: Small area details data

5.4 Mediterranee

5.4.1 Sola

(a) Time Line

(b) Time Line

Figure 35: Small area details data

6 Detailed exploration of data

Work in progress, but for example a ridge plot for each variable of a table

Also some Correlation stats, with functions to make a correlation matrix with p values and colors, and a table with linear regression coeff and signif symbols

7 Final actions and save

Rdata are saved in different files in ‘Matrices/’ folder:

  • Matrices/NEO_wshp_bo, Matrices/NEO_wshp_po, Matrices/NEO_wshp_sl, Matrices/NEO_wshp_cf : contains each SNO dataset extracted without any modification
  • NEO_wshp_sno_set_raw : contains all SNO datasets in one list without any modification
  • NEO_wshp_sno_set : contains all SNO datasets in one list without discarded tables
  • NEO_wshp_Manche, NEO_wshp_Nord_Bretagne, NEO_wshp_Sud_Bretagne, NEO_wshp_Atlantique, NEO_wshp_Mediterranee : contains datasets of all SNO filtered by large area
  • NEO_wshp_Small, NEO_wshp_Medium : contains datasets of all SNO filtered by small area in list by large and medium areas / medium in list by large area
  • NEO_wshp_plots* : contains plots created in script
  • NEO_wshp : contains all other variables

8 Supplementary data

8.1 Software details

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31 ucrt)
 os       Windows 10 x64 (build 19045)
 system   x86_64, mingw32
 ui       RTerm
 language EN
 collate  French_France.utf8
 ctype    French_France.utf8
 tz       Europe/Paris
 date     2023-04-28
 pandoc   2.19.2 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   1.1.189 @ C:\\PROGRA~1\\RStudio\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
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 [1] C:/Users/lehuen201/AppData/Local/Programs/R/R-4.2.2/library

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References

References

Cocquempot, Lucie, Christophe Delacourt, Jérôme Paillet, Philippe Riou, Jérôme Aucan, Bruno Castelle, Guillaume Charria, et al. 2019. “Coastal Ocean and Nearshore Observation: A French Case Study.” Frontiers in Marine Science 6. https://www.frontiersin.org/articles/10.3389/fmars.2019.00324.
Lheureux, A., V. David, Y. Del Amo, D. Soudant, I. Auby, F. Ganthy, H. Blanchet, et al. 2022. “Bi-Decadal Changes in Nutrient Concentrations and Ratios in Marine Coastal Ecosystems: The Case of the Arcachon Bay, France.” Progress in Oceanography 201 (February): 102740. https://doi.org/10.1016/j.pocean.2022.102740.
Liénart, Camilla, Nicolas Savoye, Yann Bozec, Elsa Breton, Pascal Conan, Valérie David, Eric Feunteun, et al. 2017. “Dynamics of Particulate Organic Matter Composition in Coastal Systems: A Spatio-Temporal Study at Multi-Systems Scale.” Progress in Oceanography 156 (August): 221–39. https://doi.org/10.1016/j.pocean.2017.03.001.
Liénart, Camilla, Nicolas Savoye, Valérie David, Pierre Ramond, Paco Rodriguez Tress, Vincent Hanquiez, Vincent Marieu, et al. 2018. “Dynamics of Particulate Organic Matter Composition in Coastal Systems: Forcing of Spatio-Temporal Variability at Multi-Systems Scale.” Progress in Oceanography 162 (March): 271–89. https://doi.org/10.1016/j.pocean.2018.02.026.
PHYTOBS. 2021. “PHYTOBS Dataset - French National Service of Observation for Phytoplankton in Coastal Waters.” https://doi.org/10.17882/85178.